# Import data
restroom_cleaned = read_csv(here::here("./Data/Public_Restrooms_20241203.csv")) %>% 
  janitor::clean_names()
## Rows: 1047 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): Facility Name, Location Type, Operator, Status, Open, Hours of Ope...
## dbl  (2): Latitude, Longitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
subway_df = read_csv(here::here("./Data/NYC_Transit_Subway_Entrance_And_Exit_Data.csv")) %>% 
  janitor::clean_names()
## Rows: 1868 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (22): Division, Line, Station Name, Route1, Route2, Route3, Route4, Rout...
## dbl  (8): Station Latitude, Station Longitude, Route8, Route9, Route10, Rout...
## lgl  (2): ADA, Free Crossover
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Clean restroom data
restroom_cleaned = restroom_cleaned %>% 
  select(
    -website, -operator, -hours_of_operation 
  ) %>% 
  rename(
    restroom_latitude = latitude,
    restroom_longitude = longitude,
    restroom_location = location
  ) %>% 
  mutate(
    restroom_latitude = as.numeric(restroom_latitude),
    restroom_longitude = as.numeric(restroom_longitude),
    restroom_location = st_as_sfc(restroom_location), #convert to point
    open = factor(
      open,
      levels = c("Future", "Seasonal", "Year Round"),
      ordered = TRUE
    ),
    accessibility = factor(
      accessibility,
      levels = c("Not Accessible", "Partially Accessible", "Fully Accessible"),
      ordered = TRUE
    ),
    changing_stations = case_when(
      changing_stations %in% c("Yes, in single-stall all gender restroom only",
                                "Yes, in women's restroom only",
                                "Yes") ~ 1,
      changing_stations == "No" ~ 0
    ),
    status = case_when(
      status %in% c("Not Operational",
                    "Closed for Construction",
                    "Closed") ~ 0,
      status == "Operational" ~ 1
    )
  ) 

# Convert dataframe to sf for spatial operations
restroom_sf = st_sf(restroom_cleaned, crs = 4326)

restroom_df = restroom_cleaned %>% 
  filter(location_type == 'Transit')
# Load the dataset
subway_df = read_csv(here::here("./Data/NYC_Transit_Subway_Entrance_And_Exit_Data.csv"))
## Rows: 1868 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (22): Division, Line, Station Name, Route1, Route2, Route3, Route4, Rout...
## dbl  (8): Station Latitude, Station Longitude, Route8, Route9, Route10, Rout...
## lgl  (2): ADA, Free Crossover
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Clean the dataset
subway_cleaned = subway_df %>% 
  janitor::clean_names() %>% 
  select(
    line, station_name, station_latitude, station_longitude, 
    route1, route2, route3, route4, route5, route6, 
    route7, route8, route9, route10, route11, 
    entry, exit_only, vending, entrance_type, ada
  ) %>%  
  mutate(across(starts_with("route"), ~ replace_na(as.character(.), ""))) %>% 
   mutate(
    station_latitude = as.numeric(station_latitude),
    station_longitude = as.numeric(station_longitude)
  )

restroom

restroom_location_bar = restroom_cleaned |> 
  group_by(location_type) |> 
  count(location_type) |> 
  mutate(loation_type = fct_reorder(location_type, n)) |> 
  plot_ly (x = ~location_type, y = ~n, color = ~location_type,
           type = "bar") |> 
  layout(
    xaxis = list(title = "Location Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_location_bar
restroom_open_bar = restroom_cleaned |> 
  filter(!is.na(open))|>
  group_by(open) |> 
  count(open, name = "n") |> 
  ungroup()|>
  mutate(open = fct_reorder(open, n)) |> 
  plot_ly (x = ~open, y = ~n, color = ~open,
           type = "bar") |> 
  layout(
    xaxis = list(title = "Open Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_open_bar
location_type_open <- 
  restroom_cleaned |> 
  filter(!is.na(open)) |> 
  filter(!is.na(location_type))|>
  group_by(location_type, open) |> 
  summarise(count = n(),.groups="drop") |> 
  group_by(open)|>
  mutate(percent = round(count / sum(count) * 100, 1))
 

location_type_open |>
  mutate(hover_text = str_c("Open: ", open, 
                            "<br>Location Type: ", location_type, 
                            "<br>Percent: ", percent, "%")) |> 
  plot_ly(
    x = ~open, 
    y = ~percent, 
    color = ~location_type,
    type = "bar", 
    colors = "viridis", 
    text = ~hover_text, 
    hoverinfo = "text"
  )|>
  layout(
    xaxis = list(title = "Open"),   
    yaxis = list(title = "Percent of Restroom (%)"))

From the plot, we can get that the open type is related to location type. All future open-status restrooms are located in library. All seasonal open-status restrooms are located in park.

restroom_accessibility_bar = restroom_cleaned |> 
  filter(!is.na(accessibility))|>
  group_by(accessibility) |> 
  count(accessibility, name = "n") |> 
  ungroup()|>
  mutate(accessibility = fct_reorder(accessibility, n)) |> 
  plot_ly (x = ~accessibility, y = ~n, color = ~accessibility,
           type = "bar") |> 
  layout(
    xaxis = list(title = "Accessibility Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_accessibility_bar
restroom_type_bar = restroom_cleaned |> 
  filter(!is.na(restroom_type))|>
  group_by(restroom_type) |> 
  count(restroom_type, name = "n") |> 
  ungroup()|>
  mutate(restroom_type = fct_reorder(restroom_type, n)) |> 
  plot_ly (x = ~restroom_type, y = ~n, color = ~restroom_type,
           type = "bar") |> 
  layout(
    xaxis = list(title = "Restroom Type"),   
    yaxis = list(title = "Number of Restrooms") 
  )

 restroom_type_bar

subway

subway_vending_bar = subway_cleaned |> 
  group_by(vending) |> 
  count(vending, name = "n") |> 
  mutate(vending = fct_reorder(vending, n)) |> 
  plot_ly (x = ~vending, y = ~n, color = ~vending, colors = "viridis",
           type = "bar") |> 
  layout(
    xaxis = list(title = "Subway Vending"),   
    yaxis = list(title = "Number of Subway Stations") 
  )

 subway_vending_bar
subway_entrance_bar = subway_cleaned |> 
  group_by(entrance_type) |> 
  count(entrance_type, name = "n") |> 
  mutate(entrance_type = fct_reorder(entrance_type, n)) |> 
  plot_ly (x = ~entrance_type, y = ~n, color = ~entrance_type, colors = "viridis",
           type = "bar") |> 
  layout(
    xaxis = list(title = "Subway Entrance Type"),   
    yaxis = list(title = "Number of Subway Stations") 
  )

 subway_entrance_bar
entrance_type_ada <- 
  subway_cleaned |> 
  group_by(entrance_type, ada) |> 
  summarise(count = n(),.groups="drop") |> 
  group_by(entrance_type)|>
  mutate(percent = round(count / sum(count) * 100, 1))

entrance_type_ada |>
  mutate(hover_text = str_c("Entrance Type: ", entrance_type, 
                            "<br>Ada: ", ada, 
                            "<br>Percent: ", percent, "%")) |> 
  plot_ly(
    x = ~entrance_type, 
    y = ~percent, 
    color = ~ada,
    type = "bar", 
    colors = "viridis", 
    text = ~hover_text, 
    hoverinfo = "text"
  )|>
  layout(
    xaxis = list(title = "Entrance Type"),   
    yaxis = list(title = "Percent of Subway Stations (%)"))

This plot show the relationship between entrance type of subway stations and their ada. The stations with walkway or elevator entrances are more likely to be considered true in ada, while stations with door, ramp, or stair entrances are more likely to be considered false in ada.